SketchRNN model released in Magenta [Hieroglyphs/Cuneiform Anyone?]

SketchRNN model released in Magenta by Douglas Eck.

From the post:

Sketch-RNN, a generative model for vector drawings, is now available in Magenta. For an overview of the model, see the Google Research blog from April 2017, Teaching Machines to Draw (David Ha). For the technical machine learning details, see the arXiv paper A Neural Representation of Sketch Drawings (David Ha and Douglas Eck).

To try out Sketch-RNN, visit the Magenta GitHub for instructions. We’ve provided trained models, code for you to train your own models in TensorFlow and a Jupyter notebook tutorial (check it out!)

The code release is timed to coincide with a Google Creative Lab data release. Visit Quick, Draw! The Data for more information. For versions of the data pre-processed to work with Sketch-RNN, please refer to the GitHub repo for more information.

We’ll leave you with a look at yoga poses generated by moving through the learned representation (latent space) of the model trained on yoga drawings. Notice how it gets confused at around 10 seconds when it moves from poses standing towards poses done on a yoga mat. In our arXiv paper A Neural Representation of Sketch Drawings we discuss reasons for this behavior.

The paper, A Neural Representation of Sketch Drawings mentions:

ShadowDraw [17] is an interactive system that predicts what a finished drawing looks like based on a set of incomplete brush strokes from the user while the sketch is being drawn. ShadowDraw used a dataset of 30K raster images combined with extracted vectorized features. In this work, we use a much larger dataset of vector sketches that is made publicly available.

ShadowDraw is described at: ShadowDraw: Real-Time User Guidance for Freehand Drawing as:

We present ShadowDraw, a system for guiding the freeform drawing of objects. As the user draws, ShadowDraw dynamically updates a shadow image underlying the user’s strokes. The shadows are suggestive of object contours that guide the user as they continue drawing. This paradigm is similar to tracing, with two major differences. First, we do not provide a single image from which the user can trace; rather ShadowDraw automatically blends relevant images from a large database to construct the shadows. Second, the system dynamically adapts to the user’s drawings in real-time and produces suggestions accordingly. ShadowDraw works by efficiently matching local edge patches between the query, constructed from the current drawing, and a database of images. A hashing technique enforces both local and global similarity and provides sufficient speed for interactive feedback. Shadows are created by aggregating the top retrieved edge maps, spatially weighted by their match scores. We test our approach with human subjects and show comparisons between the drawings that were produced with and without the system. The results show that our system produces more realistically proportioned line drawings.

My first thought was the use of such techniques to assist in copying hieroglyphs or cuneiform as such or perhaps to assist in the practice of such glyphs.

OK, that may not have been your first thought but you have to admit it would make a rocking demonstration!

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